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Record W4387266931 · doi:10.21900/j.alise.2023.1260

Advocating for Disability Access

2023· article· en· W4387266931 on OpenAlexaboutno aff
Keren Dali, Kim M. Thompson, Andrew Smith

Bibliographic record

VenueProceedings of the ALISE Annual Conference · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)AbleismTheme (computing)Disability studiesMedical educationPsychologyPublic relationsPolitical scienceSociologyLibrary scienceMedicineGender studies

Abstract

fetched live from OpenAlex

The Disabilities in LIS SIG session features presentations by American and Canadian researchers on a wide array of disability and accessibility-related topics. The first part of the session includes five 10-min research- and experience-based talks. It opens up with Kawooya, Robinson, Copeland, and Fox discussing a pilot study focused on the “Equitable Access for the Blind, Visually Impaired, and Print-Disabled (BVIPD) Students in Online Learning” (funded by USC). In “Ableism Rekindled: Experiences of Ph.D. Student During the COVID-19 Pandemic,” Dali and Charbonneau explore the experiences of disabled and neurodiverse Ph.D. students from LIS programs in Canada and the U.S. (funded by DU). To continue the theme of academia in “Discouraging Accessibility Through Poor Accessibility Training: An Antidote,” Smith addresses ways in which such training can have a negative effect on faculty and staff’s willingness to promote accessibility. Looking into professional settings, Rathbun-Grubb examines the “Post-COVID Work Experiences of Librarians with Chronic Health Conditions” based on the survey data collected in 2022. Bringing public libraries into the mix, Cahill, Adkins, Sartin Long, and Long examine why it is so challenging to engage public libraries in research-to-practice projects that improve access to library for families with young children with disabilities (funded by IMLS). Engaging a new format of blitz reports, the second part of the session highlights the news and updates on seven ongoing or recently completed projects. In their elegiac “May Be a Picture of a Dog and a Book,” Hill and Oswald share results of the study that examined the inaccessibility of social media feeds in Ontario public libraries. Guided by a critical disability studies approach, Lundy reports on preliminary findings from the dissertation-in-progress that investigates how and why individuals with non-apparent chronic illnesses share their health stories on TikTok. In the “The Prevalence of Public Library Makerspaces and Maker Programs for Youth with Disabilities,” Jung and Abbas share the initial findings from the project that investigates strategies for including youth with disabilities in public library markerspaces (funded by IMLS). Continuing this theme, Koh and Seo engage the audience in their participatory design project “Promoting Computational Thinking Skills for Blind and Visually Impaired Teens Through Accessible Library Makerspaces” (funded by IMLS). Based on the recently published co-authored study, Tobin explores “Representations of Children with Disabilities in a Public Library Board Book Collection.” In “Broadening Academic Library Employment: Neurodiversity in Academic Library Hiring,” Thompson and Dali report select findings from the pilot project focused on exploring barriers and supports in academic library hiring practices (funded by IMLS). To wrap up this stellar line-up of presentations, Phillips and Fife turn their attention to accommodation requests from academic librarians and library staff in the context of the COVID-19 pandemic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0270.025
Scholarly communication0.0140.014
Open science0.0020.031
Research integrity0.0160.019
Insufficient payload (model declined to judge)0.0290.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.381
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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